How to assess conditions for the acceptance of climate change adaptation measures by applying implementation probability Bayesian Networks in participatory processes

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

Climate change adaptation measures are best identified participatorily, yet their implementation poses challenges. While Bayesian Network (BN) modeling has been widely used to assess how adaptation measures mitigate risks, we present how to develop, in a participatory process, an innovative BN type that quantifies the implementation probability of adaptation measures by considering conditions for actors’ acceptance as well as cultural worldviews. The BN structure was derived from participatorily identified causal networks, while the conditional probability tables were straightforwardly developed with stakeholder-assigned weights. Sensitivity analysis shows how BN structure and parameters influence the BN results. We found that our approach achieves knowledge integration and learning without overwhelming stakeholders with technical details. As BNs enable exploring scenarios, stakeholders learn that many plausible futures exist. Integrating our approach in participatory adaptation processes contributes to identifying the best combinations of implementation actions, reducing the “know-do gap” in local adaptation challenges.

如何通过在参与式进程中应用实施概率贝叶斯网络来评估接受气候变化适应措施的条件
气候变化适应措施最好通过参与式方式确定,但这些措施的实施却面临挑战。虽然贝叶斯网络(BN)建模已被广泛用于评估适应措施如何降低风险,但我们介绍了如何在参与式过程中开发一种创新的 BN 类型,通过考虑参与者的接受条件和文化世界观来量化适应措施的实施概率。BN 结构源自参与式确定的因果网络,而条件概率表则通过利益相关者指定的权重直接制定。敏感性分析表明了 BN 结构和参数对 BN 结果的影响。我们发现,我们的方法既能实现知识整合和学习,又不会让利益相关者过多地了解技术细节。由于 BN 可以探索各种情景,利益相关者可以了解到存在许多似是而非的未来。将我们的方法整合到参与式适应过程中,有助于确定实施行动的最佳组合,减少当地适应挑战中的 "知行差距"。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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